Target Audience

• MCA/M.Tech Students
• Working Professionals from Corporate

Test & Evaluation

1. During the program, the participants will have to take all the assignments given to them for better learning.

2. At the end of the program, a final assessment will be conducted.

Certification

1. All successful participants will be provided with a certificate of completion.

2. Students who do not complete the course / leave it midway will not be awarded any certificate.

Tentative Date & Schedule

New batch starting from 29th June 2020 for the live online tutorial session and doubt clearing sessions

Topics to be covered

1. Introduction to Machine Learning using scikit-learn days (1-8)

• Overview of Machine Learning
• Difference between AI, ML, and DL
• Applications of ML and DL
• Types of Machine Learning
• Linear Regression
• Logistic Regression
• Overfitting and underfitting
• K-Nearest Neighbor
• Cross-validation and Hyper-parameter tuning
• Confusion Matrix, Recall, Precision
• K-Means Clustering

2. Introduction To Artificial Neural Network days (9-13)

• What is Artificial Neural Network (ANN)?
• How Neural Network Works?
• Perceptron
• Multilayer Perceptron
• Feed Forward
• Gradient Descent and Stochastic Gradient Descent
• Backpropagation

3. Introduction To Deep Learning days (14-15)

• What is Deep Learning?
• Deep Learning Packages
• Deep Learning Applications
• Building Deep Learning Environment
  • Installing TensorFlow Locally
  • Working with Google Colab

4. Indtroduction to TensorFlow days (16-18)

• What is TensorFlow?
• TensorFlow 1.x V/S TensorFlow 2.x
• Placeholder, Variables, Constants
• Operations using TensorFlow
• Difference between TensorFlow and NumPy operations
• Computational Graph
• Visualizing Graph using Tensorboard

5. Activation Functions days (19-20)

• What are Activation Functions?
• Sigmoid Function,
• Hyperbolic Tangent Function (tanh)
• ReLU – Rectified Linear Unit
• Softmax Function
• Vanishing Gradient Problem

6. Building an Artificial Neural Network: A Case Study / An Example days (21-23)

• Understanding MNIST Dataset
• Initializing weights and biases
• Defining loss/cost Function
• Train the Neural Network
• Minimizing the loss by adjusting weights and biases

7. Modern Deep Learning Optimizers and Regularization days (24-30)

• SGD with Momentum
• RMSprop
• AdaGrad
• Adam
• Dropout Layers and Regularization
• Batch Normalization

8. Building Deep Neural Network Using Keras days (31-33)

• What is Keras?
• Keras Fundamental For Deep Learning
• Keras Sequential Model and Functional API
• Solve a Linear Regression and Classification Problem with Example
• Saving and Loading a Keras Model

9. Convolutional Neural Networks (CNNs) days (34-40)

• Introduction to CNN
• CNN Architecture
• Convolutional Operations
• Pooling, Stride, and Padding Operations
• Data Augmentation
• Building, Training and Evaluating First CNN Model
• Model Performance Optimization
• Autoencoders for CNN
• Transfer Learning

10. Recurrent Neural Networks (RNNs) days (41-46)

• Introduction to RNN
• RNN Architecture
• Types of RNN
• Implementing basic RNN in TensorFlow
• Need for LSTM and GRU
• Deep RNN
• Text Classification Using LSTM

11. Projects days (47-50)

• Sentiment analysis using RNN
• MNIST Handwritten digits classification using CNN
• Cat vs Dog Image classification
• Objects Detection from Yolov3

Sample Project:

(Hand Detection)	(Cat Dog Classification)
(MNIST Digit Prediction)	(Autocomplete search query in Tensorflow)
(ChatBot Using Tensorflow)	(Neural Machine Translation NMT Using Tensorflow)
(Text Extraction from Image)